Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model

Obeidatat, Huthaifa A.; Ahmad, Imran; Rawashdeh, Mohammad R.; Abdullah, Ali A.; Shuaieb, Wafa; Obeidat, Omar; Abdallhameed, Raed A.

doi:10.1007/s11277-021-09297-z

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Also, Pala et al [118] suggest a revised technique for leading edge identification for estimating TOA of UWB signals to improve the accuracy. In a similar paper, [119] Obeidat et al investigate the effect of multiple spectrum bandwidths and carrier amounts on localization accuracy. The aim is to identify anomalous signals that negatively impact localization performance and to reduce the errors.…”

Section: ) Channel State Information (Csi)mentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

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Indoor localization (IL) is a significant topic of study with several practical applications, particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods within IoT environments. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, the survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of IoT-related industries, such as evacuationegress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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Section: ) Channel State Information (Csi)mentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

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show abstract

“…Also, Pala et al [113] suggest a revised technique to leading edge identification for estimating TOA of UWD signals to improve the accuracy. In a similar paper, [114] Obeidat et al investigate the effect of multiple spectrum bandwidths and carrier amounts on localization accuracy. The aim is to identify anomalous signals that negatively impact localization performance and to reduce the errors.…”

Section: Time Of Arrival (Toa)mentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj¹,

Himeur²,

Sohail³

et al. 2023

Preprint

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Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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“…AoA techniques are typically impractical in more typical everyday scenarios, as it is typically difficult to obtain the angular information using current conventional mobile devices such as smartphones. In this respect, distance can be calculated instead by either the Received Signal Strength (RSS) readings [ 26 , 27 ] or the ToA (sometimes referred to as Time of Flight—ToF) measurements [ 28 , 29 ]. A limiting factor for the ToA case is that the receiving and transmitting clocks must be synchronized in order to accurately estimate the ToA and produce more precise distance estimates.…”

Section: 3d Localization Techniquesmentioning

confidence: 99%

A Survey of 3D Indoor Localization Systems and Technologies

Sesyuk

Ioannou

Raspopoulos

2022

Sensors

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Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization.

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Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model

Cited by 5 publications

References 21 publications

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

A Survey of 3D Indoor Localization Systems and Technologies

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